PubMed · 10609738
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R G Newcombe. 1999. The value of the negative predictive values.. https://pubmed.ncbi.nlm.nih.gov/10609738/
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An area that requires further research is how best to measure test method performance in validation studies and how to set criteria that should be used to judge the adequacy of this performance. The studies reported here were designed to begin an investigation of these questions. Computer simulations were used to generate data sets similar to those that might be obtained from a large validation study. These data were then analysed using three procedures including determination of the 95% prediction interval (PI), calculation of Pearson's correlation coefficient and calculation of the contingent probability statistics (CPS), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The results of this work suggest that of the three approaches examined, quantitative measurements with calculation of the 95% PI provide the most information to allow discrimination between the performance of several different NTMs. The results also suggest that dividing data sets into positive and negative toxicity classifications followed by the calculation of CPS leads to considerable information loss. This loss of information may be so significant that it is not possible in certain circumstances to distinguish between NTMs that are adequate and those that are not.
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A measure of the predictive capability of a proportional hazards regression is derived. The measure is based on the residuals appropriate to proportional hazards regression. A population version is presented and can be seen not to depend on the censoring mechanism under the provision that any such censoring be independent or conditionally independent of the failure mechanism given the covariate. For the special case of a Weibull regression model, for which the covariate distribution follows binary, uniform, normal, or exponential laws, we derive analytic results. These alone give credence to the measure which can be seen to reflect strength of regression effect, as quantified by the parameter estimate, although on a scale between 0 and 1, independently of the intercept or shape parameter of the particular Weibull law and only weakly dependent on the covariate distribution. Extensions to partial and multiple measures of predictive ability are straightforward. An example is provided.